The next generation of workers is facing a new obstacle. And surprisingly, it isn’t AI. New research from the American University found that 78 percent of Americans say AI isn’t covered — or is actively discouraged — in high school, leaving many anxious about what this technology could mean for their careers.
The worst part? This lack of training and support has left many young Americans who have not used AI feeling more pessimistic than ever about job prospects and access to opportunities.
The bigger question I keep coming back to: Is this purely a coincidence or is something deeper going on within the education system?
Why Are Schools Failing to Teach AI?
Institutions develop courses slowly over long periods, while AI technology advances rapidly, sometimes in months or weeks. By the time programs adapt, the required skills have already shifted, creating a systemic lag.
Why Institutions Are Falling Behind
Despite nearly three in four young Americans saying AI literacy matters for their careers, educational institutions are largely failing to deliver on AI education. And this isn’t just a temporary lag that curriculum updates will fix — the issue is systemic.
Many institutions develop academic courses over long periods of time, but AI technology is advancing rapidly over the course of months, if not weeks. By the time institutions adapt their programs, the relevant skills are already shifting.
This creates a fundamental problem. Traditional education operates on a learn-then-do model where students spend years gaining knowledge before applying it professionally. AI disrupts this model.
With AI, students can quickly apply what they learn. The technology not only allows them to keep practicing but also provides immediate access to expert-level guidance and the ability to produce professional-grade work without needing formal credentials.
The result is a dramatic acceleration in knowledge for those who engage with these tools. Someone who started using AI six months ago can demonstrate skills that would have taken years to develop in the traditional model.
New data from GitHub and Microsoft found that developers were able to code more than 50 percent faster by using AI tools, with the greatest increase in productivity among juniors. Similarly, a Stanford University study revealed that tutors who used generative AI saw improvements in student learning outcomes (4 percent compared to 9 percent), with beginners benefiting most.
Level Up Beyond Your Credentials
The accelerated adoption of AI forces a shift in how new job prospects are evaluated. The traditional hiring model relies on credentials as proxy signals: degrees from particular institutions, grade point averages and previous employer names. These proxies work when they correlate reliability with actual capability. More to the point, for a long time, proxies were all we had to go by.
Now, however, individuals can build and demonstrate competence outside institutional frameworks and in ways that modern hiring processes miss. Rather than just inferring capability from where someone studied, employers can evaluate what someone has actually built, written, analyzed or shipped.
This shift matters particularly in a context where, as the reality of sophisticated AI-generated content becomes more apparent, claims without evidence lose credibility. Public work provides verifiable proof of what someone can do.
For individuals, this means the path to demonstrating capability no longer requires waiting for institutional permission or traditional career progression. AI tools enable the production of portfolio-quality work immediately. The constraint shifts from access to resources toward a willingness to build and share publicly.
The Widening Divide
Those who have experienced how AI accelerates learning and enables demonstration likely get why the traditional credential-first path is outdated. Those who haven’t engaged with the technology sense disruption as well, but cannot meaningfully take advantage of it.
For the 78 percent whose education didn’t prepare them for this shift, waiting for institutions to catch up will lead to negative disadvantages. The pressure to adapt exists now, not at some future point when educational systems have caught up.
How Employers And Individuals Can Adapt
For individuals hesitant about engaging with AI, the data provides clear answers. Concerns about AI replacing jobs carry less risk than avoiding the technology entirely. The American University survey shows non-AI users are more pessimistic than those who have used it, suggesting that lack of experience rather than the technology itself drives a negative outlook.
To counter this, individuals must be encouraged and supported to treat AI as an accelerant for learning and showing their skills rather than a threat to their jobs. In practice, this looks like educators and employers setting clear expectations for which tools individuals can use, how they can use them and when their use isn’t appropriate. The environment must encourage experimentation in the open, sharing feedback together, and identifying ways for everyone to use AI and showcase their true abilities and skills. They need to be able to experiment without fear of repercussions when experiments fail or of training AI to ultimately make their own educations or roles obsolete.
According to a study from Carnegie Mellon University, AI dramatically accelerates learning, with student writing quality jumping a full letter grade when students used generative AI with proper guidance on how to use it effectively. Because of this acceleration, anyone starting today can build capability and close gaps far faster than traditional career paths would suggest.
The shift also requires new means of evaluation, especially given that individuals are increasingly using AI for writing essays, job applications and creating resumes and cover letters, therefore lowering the bar for these as signals of candidate quality. A study by Dartmouth College and Princeton University scholars found that, since the onset of AI, the most qualified candidates are hired 19 percent less often compared to the least qualified candidates, who are now hired 14 percent more often.
Now, rather than filtering candidates through these increasingly unreliable filters, effective hiring will involve assessing larger quantities of direct evidence like portfolios, public work and documented problem-solving approaches. This means developing tools and processes that evaluate behavioral signals and demonstrate capability at scale, rather than depending on name-brand institutions to pre-filter talent.
The Big AI Caveat
Although I leaning heavily in support of AI, it’s worth being cognizant of its limitations. Recall the Microsoft study, which, like its Stanford counterpart, showed that the benefits of AI-accelerated learning ultimately levels out as folks reach intermediate or higher expertise.
Another study showed the stark disconnect between expectations and reality for developers hoping to reap the benefits of AI. Although the engineers observed believed AI had sped them up by an average of 24 percent, results indicated that using AI tools had actually reduced their output by 19 percent. Although AI helps many people learn and work faster and smarter when used properly, the reality is that the technology is not perfect, especially as we evolve our habits alongside it.
The Path Forward: Embracing AI To Get Ahead
The American University survey captures a transition point. One cohort experiences traditional educational paths that don’t prepare them for AI-enabled work. Another cohort experiments with these tools directly and discovers accelerated paths to capability. The gap between these groups isn’t static. It’s widening as those who engage with AI compound their learning advantages.
Educational institutions face pressure that they likely cannot resolve quickly, given their structural constraints. This suggests that individual adaptation, rather than institutional reform, will determine who thrives in this transition. The technology that enables this acceleration is already available. The choice facing individuals is whether to engage with it now or watch the experience gap widen further. Direct experience with AI tools shifts perspective from threat to opportunity. For those still hesitant, that shift in outlook may be the most compelling reason to start dipping one’s toes today.
